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AI
DentalCall AI Reception
An AI voice receptionist for dental clinics
Client
Confidential
Duration
6 months
Category
AI
Year
2024
Technology
AWS, Twilio, OpenAI API, Whisper ASR, ElevenLabs TTS, NodeJS, ExpressJS, MongoDB Atlas
Introduction
DentalCall AI Reception is an AI voice receptionist for dental clinics, handling calls, booking appointments, answering typical queries. This platform acts as an intelligent front-desk extension, ensuring every patient call is answered with professional clarity while clinical staff focus on providing high-quality dental care.
Challenges
DentalCall AI Reception aims to eliminate the operational bottlenecks that prevent dental clinics from scaling. However, several challenges stood in the way of achieving this goal:
– Audio latency and speech naturalness: Traditional automated systems often suffer from delays and unnatural cadences, leading to patient frustration and high call-drop rates.
– Accurate clinical transcription: Capturing precise dental terminology and patient details (like specific tooth pain or procedure names) in noisy environments proved difficult for standard voice-to-text engines.
– Complex multi-platform synchronization: Coordinating real-time appointment availability across fragmented scheduling systems while maintaining a consistent state between the voice agent and the clinic’s database.
– Scalability during peak rushes: Managing sudden bursts of morning calls or emergency inquiries without performance degradation or infrastructure bottlenecks.
Solutions
To overcome the challenges, Hola Tech adhered to the best practices. Key components of the solution included:
– Implement a real-time voice pipeline: Utilize Twilio for robust telephony streaming and ElevenLabs TTS to deliver human-like, empathetic voice responses. By optimizing the websocket connection between Twilio and the backend, the system achieves sub-second response times, creating a conversational flow that feels natural and trustworthy to patients.
– Optimize medical speech recognition: Integrate the Whisper ASR model to transcribe patient speech with high accuracy, even when dental-specific jargon is used. This layer ensures that the OpenAI API receives clean, contextually rich text, allowing the AI to accurately identify the intent.
– Engineer a scalable backend: Develop an event-driven middleware using NodeJS and ExpressJS to orchestrate the flow of data between the voice engine and the clinic’s management tools. This architecture allows the system to handle hundreds of concurrent calls by delegating heavy processing tasks to asynchronous workers, ensuring zero downtime.
Featured numbers
– Achieved a 98% accuracy rate in transcribing dental terminology through a fine-tuned Whisper ASR implementation
– Handled 100% of after-hours and overflow calls, capturing an average of 15 new patient leads per week that were previously lost to voicemail
– Decreased administrative scheduling time by 80% through direct, automated calendar synchronization
Results
The implemented system successfully met DentalCall AI Reception’s requirements for performance, security, and medical-grade reliability. The platform has significantly lowered the barrier to efficient patient management, empowering clinics to operate more profitably and with less stress, with over 20,000 successful patient interactions already processed. This demonstrates the project’s success in providing a professional, accessible, and technologically superior solution for the healthcare industry.
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